DEVICE = torch.device("cuda:0" if torch.cuda.is_available() and args.use_cuda else "cpu")

if args.use_cuda and torch.cuda.is_available():
    torch.backends.cudnn.benchmark = True
    logging.info("Use Cuda.")


def train(loader, net, criterion, optimizer, device, debug_steps=100, epoch=-1):
    net.train(True)
    running_loss = 0.0
    running_regression_loss = 0.0
    running_classification_loss = 0.0
    for i, data in enumerate(loader):
        images, boxes, labels = data
        images = images.to(device)
        boxes = boxes.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        confidence, locations = net(images)
        regression_loss, classification_loss = criterion(confidence, locations, labels, boxes)  # TODO CHANGE BOXES
        loss = regression_loss + classification_loss
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        running_regression_loss += regression_loss.item()
        running_classification_loss += classification_loss.item()
        if i and i % debug_steps == 0:
            avg_loss = running_loss / debug_steps
            avg_reg_loss = running_regression_loss / debug_steps
            avg_clf_loss = running_classification_loss / debug_steps
#             logging.info(
#                 f"Epoch: {epoch}, Step: {i}, " +
#                 f"Average Loss: {avg_loss:.4f}, " +
#                 f"Average Regression Loss {avg_reg_loss:.4f}, " +
#                 f"Average Classification Loss: {avg_clf_loss:.4f}"
#             )
            running_loss = 0.0
            running_regression_loss = 0.0
            running_classification_loss = 0.0
    return avg_loss


def test(loader, net, criterion, device):
    net.eval()
    running_loss = 0.0
    running_regression_loss = 0.0
    running_classification_loss = 0.0
    num = 0
    for _, data in enumerate(loader):
        images, boxes, labels = data
        images = images.to(device)
        boxes = boxes.to(device)
        labels = labels.to(device)
        num += 1

        with torch.no_grad():
            confidence, locations = net(images)
            regression_loss, classification_loss = criterion(confidence, locations, labels, boxes)
            loss = regression_loss + classification_loss

        running_loss += loss.item()
        running_regression_loss += regression_loss.item()
        running_classification_loss += classification_loss.item()
    return running_loss / num, running_regression_loss / num, running_classification_loss / num


def mymain():
#if __name__ == '__main__':
    timer = Timer()
    liveloss = PlotLosses()
    logs = {}
    logging.info(args)
    if args.net == 'vgg16-ssd':
        create_net = create_vgg_ssd
        config = vgg_ssd_config
    elif args.net == 'mb1-ssd':
        create_net = create_mobilenetv1_ssd
        config = mobilenetv1_ssd_config
    elif args.net == 'mb1-ssd-lite':
        create_net = create_mobilenetv1_ssd_lite
        config = mobilenetv1_ssd_config
    elif args.net == 'sq-ssd-lite':
        create_net = create_squeezenet_ssd_lite
        config = squeezenet_ssd_config
    elif args.net == 'mb2-ssd-lite':
        create_net = lambda num: create_mobilenetv2_ssd_lite(num, width_mult=args.mb2_width_mult)
        config = mobilenetv1_ssd_config
    else:
        logging.fatal("The net type is wrong.")
        parser.print_help(sys.stderr)
        sys.exit(1)
    train_transform = TrainAugmentation(config.image_size, config.image_mean, config.image_std)
    target_transform = MatchPrior(config.priors, config.center_variance,
                                  config.size_variance, 0.5)

    test_transform = TestTransform(config.image_size, config.image_mean, config.image_std)

    logging.info("Prepare training datasets.")
    datasets = []
    for dataset_path in args.datasets:
        if args.dataset_type == 'voc':
            dataset = VOCDataset(dataset_path, transform=train_transform,
                                 target_transform=target_transform)
            label_file = os.path.join(args.checkpoint_folder, "voc-model-labels.txt")
            store_labels(label_file, dataset.class_names)
            num_classes = len(dataset.class_names)
        elif args.dataset_type == 'open_images':
            dataset = OpenImagesDataset(dataset_path,
                 transform=train_transform, target_transform=target_transform,
                 dataset_type="train", balance_data=args.balance_data)
            label_file = os.path.join(args.checkpoint_folder, "open-images-model-labels.txt")
            store_labels(label_file, dataset.class_names)
            logging.info(dataset)
            num_classes = len(dataset.class_names)

        else:
            raise ValueError(f"Dataset tpye {args.dataset_type} is not supported.")
        datasets.append(dataset)
    logging.info(f"Stored labels into file {label_file}.")
    train_dataset = ConcatDataset(datasets)
    logging.info("Train dataset size: {}".format(len(train_dataset)))
    train_loader = DataLoader(train_dataset, args.batch_size,
                              num_workers=args.num_workers,
                              shuffle=True)
    logging.info("Prepare Validation datasets.")
    if args.dataset_type == "voc":
        val_dataset = VOCDataset(args.validation_dataset, transform=test_transform,
                                 target_transform=target_transform, is_test=True)
    elif args.dataset_type == 'open_images':
        val_dataset = OpenImagesDataset(dataset_path,
                                        transform=test_transform, target_transform=target_transform,
                                        dataset_type="test")
        logging.info(val_dataset)
    logging.info("validation dataset size: {}".format(len(val_dataset)))

    val_loader = DataLoader(val_dataset, args.batch_size,
                            num_workers=args.num_workers,
                            shuffle=False)
    logging.info("Build network.")
    net = create_net(num_classes)
    min_loss = -10000.0
    last_epoch = -1

    base_net_lr = args.base_net_lr if args.base_net_lr is not None else args.lr
    extra_layers_lr = args.extra_layers_lr if args.extra_layers_lr is not None else args.lr
    if args.freeze_base_net:
        logging.info("Freeze base net.")
        freeze_net_layers(net.base_net)
        params = itertools.chain(net.source_layer_add_ons.parameters(), net.extras.parameters(),
                                 net.regression_headers.parameters(), net.classification_headers.parameters())
        params = [
            {'params': itertools.chain(
                net.source_layer_add_ons.parameters(),
                net.extras.parameters()
            ), 'lr': extra_layers_lr},
            {'params': itertools.chain(
                net.regression_headers.parameters(),
                net.classification_headers.parameters()
            )}
        ]
    elif args.freeze_net:
        freeze_net_layers(net.base_net)
        freeze_net_layers(net.source_layer_add_ons)
        freeze_net_layers(net.extras)
        params = itertools.chain(net.regression_headers.parameters(), net.classification_headers.parameters())
        logging.info("Freeze all the layers except prediction heads.")
    else:
        params = [
            {'params': net.base_net.parameters(), 'lr': base_net_lr},
            {'params': itertools.chain(
                net.source_layer_add_ons.parameters(),
                net.extras.parameters()
            ), 'lr': extra_layers_lr},
            {'params': itertools.chain(
                net.regression_headers.parameters(),
                net.classification_headers.parameters()
            )}
        ]

    timer.start("Load Model")
    if args.resume:
        logging.info(f"Resume from the model {args.resume}")
        net.load(args.resume)
    elif args.base_net:
        logging.info(f"Init from base net {args.base_net}")
        net.init_from_base_net(args.base_net)
    elif args.pretrained_ssd:
        logging.info(f"Init from pretrained ssd {args.pretrained_ssd}")
        net.init_from_pretrained_ssd(args.pretrained_ssd)
    logging.info(f'Took {timer.end("Load Model"):.2f} seconds to load the model.')

    net.to(DEVICE)

    criterion = MultiboxLoss(config.priors, iou_threshold=0.5, neg_pos_ratio=3,
                             center_variance=0.1, size_variance=0.2, device=DEVICE)
    optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum,
                                weight_decay=args.weight_decay)
    logging.info(f"Learning rate: {args.lr}, Base net learning rate: {base_net_lr}, "
                 + f"Extra Layers learning rate: {extra_layers_lr}.")

    if args.scheduler == 'multi-step':
        logging.info("Uses MultiStepLR scheduler.")
        milestones = [int(v.strip()) for v in args.milestones.split(",")]
        scheduler = MultiStepLR(optimizer, milestones=milestones,
                                                     gamma=0.1, last_epoch=last_epoch)
    elif args.scheduler == 'cosine':
        logging.info("Uses CosineAnnealingLR scheduler.")
        scheduler = CosineAnnealingLR(optimizer, args.t_max, last_epoch=last_epoch)
    else:
        logging.fatal(f"Unsupported Scheduler: {args.scheduler}.")
        parser.print_help(sys.stderr)
        sys.exit(1)

    logging.info(f"Start training from epoch {last_epoch + 1}.")
    for epoch in range(last_epoch + 1, args.num_epochs):
        scheduler.step()
        avg_loss = train(train_loader, net, criterion, optimizer,
              device=DEVICE, debug_steps=args.debug_steps, epoch=epoch)
        logs['train loss']=avg_loss
        
        if epoch % args.validation_epochs == 0 or epoch == args.num_epochs - 1:
            val_loss, val_regression_loss, val_classification_loss = test(val_loader, net, criterion, DEVICE)
#             logging.info(
#                 f"Epoch: {epoch}, " +
#                 f"Validation Loss: {val_loss:.4f}, " +
#                 f"Validation Regression Loss {val_regression_loss:.4f}, " +
#                 f"Validation Classification Loss: {val_classification_loss:.4f}"
#             )
            model_path = os.path.join(args.checkpoint_folder, f"{args.net}-Epoch-{epoch}-Loss-{val_loss}.pth")
            net.save(model_path)
            logging.info(f"Saved model {model_path}")
        liveloss.update(logs)
        liveloss.draw()

mymain()